Each year, farmers incur substantial losses from weather, crop disease, pests, and poor field management. Growing crops at large scale creates many challenges for growers, agricultural retailers and consultants, and agricultural pesticide distributors and manufacturers. Consistent and accurate monitoring of crops is desirable across all growth stages thereof. Current methods of crop health monitoring are manually intensive, time consuming, and prone to error. However, it is difficult to properly monitor crops for insects, disease, plant nutritional deficiencies, and environmental effects due, in part, to the large size of individual fields, large scale of farm operations, and lack of available labor in the agricultural industry. Currently, in order to monitor crops, growers perform physical crop scouting during which a human walks the fields and makes manual observations of the crops. Alternatively, remote sensing methods such as satellite, manned aircraft, and/or unmanned aerial vehicles may be used to monitor crops.
Physical crop scouting does not allow for timely or high resolution monitoring simply because a human cannot efficiently cover the extent of agricultural land. Physical crop scouting may be challenging to accomplish due to the size and density of the crop (e.g. 10 foot tall corn) as well as environmental conditions such as water logged soils. Human laborers are relatively less productive in harsh climates due to heat, humidity, and other weather conditions. Random scouting (e.g., walks through the fields) may produce a small sample representation of the field, and regression modeling based thereon may establish remedial recommendations. Industry evidence suggests that random scouting often covers less than 10% of the field area while many other fields go un-sampled. Therefore, the farmer may miss early warning indications of crop loss or yield limiting factors. Similarly, remote sensing methods have associated difficulties. While remote sensing may quickly monitor large areas, it only captures the reflectance of light, standard imagery, and other sensor data available from above the crop.
Crop and vegetation observations from satellites, planes, and unmanned aerial vehicles (e.g., drones) coupled with weather and other historical field and environmental data may be used as inputs into data science prediction models that provide a probability of diagnoses. Despite significant investments in this arena, adoption of these agricultural models and implementation of actions based on predictive modeling results are relatively infrequent. Remote sensing may capture plant stress, but may not capture the true cause of crop health decline. In view of these challenges, many growers use a combination of remote sensing and physical scouting. However, implementing multiple crop monitoring methods increases the time between data collection and grower action. Timely crop monitoring is critical to minimize the effect of yield loss. To optimally preserve yield, crops should be monitored weekly, but growers may struggle to properly monitor a crop even once a growing season.
For agricultural retailers and consultants, current scouting methods limit the expansion of business and impede the service provided to growers. These retailers depend on the sale of fungicides, insecticides, and other crop protection products. However, if crop stresses are not uncovered through monitoring, crop protection products cannot be prescribed to counter yield-affecting stresses thereby decreasing potential sales for the retailers.
Further, agricultural distributors and manufacturers do not know where and when insects and diseases will affect crops across a geographic region during the growing season because there is not a timely, accurate, geo-referenced report of in-season crop stresses. Such a report is desirable in the art, and distributors and manufactures could use such information to gain efficiencies in warehousing, distribution, and sales of crop-treating products. A crop monitoring system that is timely, georeferenced, scales to large areas, and determines the cause of crop stress represents an improvement in crop monitoring practices.
The description provided in the background section should not be assumed to be prior art merely because it is mentioned in or associated with the background section. The background section may include information that describes one or more aspects of the subject technology.